Overview

Dataset statistics

Number of variables13
Number of observations125
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.8 KiB
Average record size in memory105.0 B

Variable types

Numeric11
Categorical2

Alerts

Country has a high cardinality: 125 distinct values High cardinality
Happiness is highly correlated with GDP and 2 other fieldsHigh correlation
GDP is highly correlated with Happiness and 2 other fieldsHigh correlation
SocialSupport is highly correlated with Happiness and 3 other fieldsHigh correlation
Health is highly correlated with Happiness and 2 other fieldsHigh correlation
Freedom is highly correlated with PositiveAffectHigh correlation
PositiveAffect is highly correlated with FreedomHigh correlation
NegativeAffect is highly correlated with SocialSupportHigh correlation
Happiness is highly correlated with GDP and 2 other fieldsHigh correlation
GDP is highly correlated with Happiness and 2 other fieldsHigh correlation
SocialSupport is highly correlated with Happiness and 3 other fieldsHigh correlation
Health is highly correlated with Happiness and 2 other fieldsHigh correlation
Freedom is highly correlated with PositiveAffectHigh correlation
PositiveAffect is highly correlated with FreedomHigh correlation
NegativeAffect is highly correlated with SocialSupportHigh correlation
Happiness is highly correlated with GDP and 2 other fieldsHigh correlation
GDP is highly correlated with Happiness and 2 other fieldsHigh correlation
SocialSupport is highly correlated with Happiness and 1 other fieldsHigh correlation
Health is highly correlated with Happiness and 1 other fieldsHigh correlation
Happiness is highly correlated with GDP and 7 other fieldsHigh correlation
GDP is highly correlated with Happiness and 5 other fieldsHigh correlation
SocialSupport is highly correlated with Happiness and 3 other fieldsHigh correlation
Health is highly correlated with Happiness and 4 other fieldsHigh correlation
Freedom is highly correlated with Generosity and 2 other fieldsHigh correlation
Generosity is highly correlated with Happiness and 4 other fieldsHigh correlation
Corruption is highly correlated with Happiness and 3 other fieldsHigh correlation
PositiveAffect is highly correlated with Happiness and 2 other fieldsHigh correlation
NegativeAffect is highly correlated with Happiness and 2 other fieldsHigh correlation
ConfidenceInGovernment is highly correlated with GDP and 3 other fieldsHigh correlation
Region is highly correlated with Happiness and 6 other fieldsHigh correlation
Country is uniformly distributed Uniform
df_index has unique values Unique
Country has unique values Unique
Happiness has unique values Unique
SocialSupport has unique values Unique
Freedom has unique values Unique
PositiveAffect has unique values Unique
NegativeAffect has unique values Unique

Reproduction

Analysis started2022-04-11 17:19:18.649877
Analysis finished2022-04-11 17:20:06.535664
Duration47.89 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.4
Minimum1
Maximum146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-04-11T19:20:06.733043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.2
Q138
median74
Q3110
95-th percentile139.8
Maximum146
Range145
Interquartile range (IQR)72

Descriptive statistics

Standard deviation42.63990256
Coefficient of variation (CV)0.5731169699
Kurtosis-1.18714351
Mean74.4
Median Absolute Deviation (MAD)36
Skewness-0.001505416924
Sum9300
Variance1818.16129
MonotonicityStrictly increasing
2022-04-11T19:20:06.875339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.8%
931
 
0.8%
1091
 
0.8%
1081
 
0.8%
1071
 
0.8%
1061
 
0.8%
1051
 
0.8%
1041
 
0.8%
1031
 
0.8%
1021
 
0.8%
Other values (115)115
92.0%
ValueCountFrequency (%)
11
0.8%
31
0.8%
41
0.8%
51
0.8%
61
0.8%
71
0.8%
81
0.8%
91
0.8%
101
0.8%
111
0.8%
ValueCountFrequency (%)
1461
0.8%
1451
0.8%
1441
0.8%
1431
0.8%
1421
0.8%
1411
0.8%
1401
0.8%
1391
0.8%
1381
0.8%
1371
0.8%

Country
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Cameroon
 
1
Burkina Faso
 
1
Hungary
 
1
Mexico
 
1
Thailand
 
1
Other values (120)
120 

Length

Max length23
Median length7
Mean length8.208
Min length4

Characters and Unicode

Total characters1026
Distinct characters51
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)100.0%

Sample

1st rowAlbania
2nd rowArgentina
3rd rowArmenia
4th rowAustralia
5th rowAustria

Common Values

ValueCountFrequency (%)
Cameroon1
 
0.8%
Burkina Faso1
 
0.8%
Hungary1
 
0.8%
Mexico1
 
0.8%
Thailand1
 
0.8%
Mongolia1
 
0.8%
Bahrain1
 
0.8%
Uruguay1
 
0.8%
Venezuela1
 
0.8%
Kyrgyzstan1
 
0.8%
Other values (115)115
92.0%

Length

2022-04-11T19:20:07.022863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united3
 
2.0%
republic2
 
1.4%
congo2
 
1.4%
and2
 
1.4%
south2
 
1.4%
uganda1
 
0.7%
bolivia1
 
0.7%
colombia1
 
0.7%
australia1
 
0.7%
niger1
 
0.7%
Other values (132)132
89.2%

Most occurring characters

ValueCountFrequency (%)
a166
16.2%
i99
 
9.6%
n77
 
7.5%
o63
 
6.1%
e62
 
6.0%
r56
 
5.5%
t39
 
3.8%
l35
 
3.4%
u34
 
3.3%
s34
 
3.3%
Other values (41)361
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter853
83.1%
Uppercase Letter146
 
14.2%
Space Separator23
 
2.2%
Close Punctuation2
 
0.2%
Open Punctuation2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a166
19.5%
i99
11.6%
n77
9.0%
o63
 
7.4%
e62
 
7.3%
r56
 
6.6%
t39
 
4.6%
l35
 
4.1%
u34
 
4.0%
s34
 
4.0%
Other values (16)188
22.0%
Uppercase Letter
ValueCountFrequency (%)
M13
 
8.9%
S12
 
8.2%
C12
 
8.2%
T11
 
7.5%
B11
 
7.5%
L9
 
6.2%
A9
 
6.2%
K9
 
6.2%
P8
 
5.5%
G7
 
4.8%
Other values (12)45
30.8%
Space Separator
ValueCountFrequency (%)
23
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin999
97.4%
Common27
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a166
16.6%
i99
 
9.9%
n77
 
7.7%
o63
 
6.3%
e62
 
6.2%
r56
 
5.6%
t39
 
3.9%
l35
 
3.5%
u34
 
3.4%
s34
 
3.4%
Other values (38)334
33.4%
Common
ValueCountFrequency (%)
23
85.2%
)2
 
7.4%
(2
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a166
16.2%
i99
 
9.6%
n77
 
7.5%
o63
 
6.1%
e62
 
6.0%
r56
 
5.5%
t39
 
3.8%
l35
 
3.4%
u34
 
3.3%
s34
 
3.3%
Other values (41)361
35.2%

Happiness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.413383026
Minimum3.253560066
Maximum7.476213932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-04-11T19:20:07.155017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.253560066
5-th percentile3.844961357
Q14.62813282
median5.552915096
Q36.174117565
95-th percentile7.067343616
Maximum7.476213932
Range4.222653866
Interquartile range (IQR)1.545984745

Descriptive statistics

Standard deviation0.9926575236
Coefficient of variation (CV)0.183371012
Kurtosis-0.7419596261
Mean5.413383026
Median Absolute Deviation (MAD)0.7705321312
Skewness-0.07442770553
Sum676.6728783
Variance0.9853689592
MonotonicityNot monotonic
2022-04-11T19:20:07.286798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5048811441
 
0.8%
5.9106764791
 
0.8%
5.650552751
 
0.8%
6.0899047851
 
0.8%
5.8308706281
 
0.8%
5.6468524931
 
0.8%
3.4168629651
 
0.8%
4.6468911171
 
0.8%
5.4813108441
 
0.8%
5.3338503841
 
0.8%
Other values (115)115
92.0%
ValueCountFrequency (%)
3.2535600661
0.8%
3.3471212391
0.8%
3.4168629651
0.8%
3.5048811441
0.8%
3.638300181
0.8%
3.7953007221
0.8%
3.8238656521
0.8%
3.9293441771
0.8%
3.9327774051
0.8%
4.0005168911
0.8%
ValueCountFrequency (%)
7.4762139321
0.8%
7.3310360911
0.8%
7.2937278751
0.8%
7.257037641
0.8%
7.225181581
0.8%
7.1032733921
0.8%
7.0743246081
0.8%
7.0394196511
0.8%
6.99175931
0.8%
6.9283475881
0.8%

GDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct122
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.23342786
Minimum6.623783588
Maximum11.11681843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-04-11T19:20:07.428730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6.623783588
5-th percentile7.257780933
Q18.493188858
median9.439295769
Q310.11722469
95-th percentile10.72316017
Maximum11.11681843
Range4.49303484
Interquartile range (IQR)1.624035835

Descriptive statistics

Standard deviation1.109893975
Coefficient of variation (CV)0.120203893
Kurtosis-0.6361418462
Mean9.23342786
Median Absolute Deviation (MAD)0.7984075546
Skewness-0.4833447467
Sum1154.178483
Variance1.231864635
MonotonicityNot monotonic
2022-04-11T19:20:07.564133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4921264654
 
3.2%
7.9714007381
 
0.8%
10.724075321
 
0.8%
8.4931888581
 
0.8%
10.489561081
 
0.8%
9.4392957691
 
0.8%
7.6003651621
 
0.8%
10.590446471
 
0.8%
8.5795001981
 
0.8%
9.1634387971
 
0.8%
Other values (112)112
89.6%
ValueCountFrequency (%)
6.6237835881
0.8%
6.6947269441
0.8%
6.8308744431
0.8%
6.9985480311
0.8%
7.0353589061
0.8%
7.2372751241
0.8%
7.2559013371
0.8%
7.265299321
0.8%
7.3535728451
0.8%
7.4104514121
0.8%
ValueCountFrequency (%)
11.116818431
0.8%
11.090271951
0.8%
10.93408681
0.8%
10.900905611
0.8%
10.800501821
0.8%
10.746841431
0.8%
10.724075321
0.8%
10.719499591
0.8%
10.706581121
0.8%
10.675693511
0.8%

SocialSupport
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8100448117
Minimum0.5078052282
Maximum0.9667528272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-04-11T19:20:07.698438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.5078052282
5-th percentile0.6347601175
Q10.7399561405
median0.8286458254
Q30.9002557993
95-th percentile0.9348479986
Maximum0.9667528272
Range0.4589475989
Interquartile range (IQR)0.1602996588

Descriptive statistics

Standard deviation0.103022704
Coefficient of variation (CV)0.1271814874
Kurtosis-0.3986917131
Mean0.8100448117
Median Absolute Deviation (MAD)0.07604843378
Skewness-0.6770351193
Sum101.2556015
Variance0.01061367754
MonotonicityNot monotonic
2022-04-11T19:20:07.855918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.94213110211
 
0.8%
0.80692994591
 
0.8%
0.88119983671
 
0.8%
0.71460431811
 
0.8%
0.87674754861
 
0.8%
0.94175457951
 
0.8%
0.91979122161
 
0.8%
0.82895261051
 
0.8%
0.93749529121
 
0.8%
0.65544050931
 
0.8%
Other values (115)115
92.0%
ValueCountFrequency (%)
0.50780522821
0.8%
0.55542272331
0.8%
0.58210957051
0.8%
0.59049516921
0.8%
0.60676747561
0.8%
0.6263319851
0.8%
0.63402557371
0.8%
0.63769829271
0.8%
0.63822638991
0.8%
0.64119309191
0.8%
ValueCountFrequency (%)
0.96675282721
0.8%
0.94995784761
0.8%
0.94213110211
0.8%
0.94175457951
0.8%
0.93749529121
0.8%
0.93733179571
0.8%
0.93568634991
0.8%
0.93149459361
0.8%
0.92818784711
0.8%
0.92631661891
0.8%

Health
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct98
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.058293
Minimum47.29999924
Maximum75.90731812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-04-11T19:20:07.990410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum47.29999924
5-th percentile52.92000122
Q159.20000076
median66
Q368.30000305
95-th percentile73.30000305
Maximum75.90731812
Range28.60731888
Interquartile range (IQR)9.100002289

Descriptive statistics

Standard deviation6.494142868
Coefficient of variation (CV)0.1013786438
Kurtosis-0.3841754874
Mean64.058293
Median Absolute Deviation (MAD)3.599998474
Skewness-0.5778281035
Sum8007.286625
Variance42.17389159
MonotonicityNot monotonic
2022-04-11T19:20:08.126179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.599998475
 
4.0%
63.799999243
 
2.4%
663
 
2.4%
673
 
2.4%
68.400001533
 
2.4%
67.800003052
 
1.6%
59.200000762
 
1.6%
68.599998472
 
1.6%
66.900001532
 
1.6%
61.799999242
 
1.6%
Other values (88)98
78.4%
ValueCountFrequency (%)
47.299999241
0.8%
48.900001531
0.8%
49.200000761
0.8%
49.299999241
0.8%
51.200000761
0.8%
51.900001531
0.8%
52.900001531
0.8%
531
0.8%
53.200000761
0.8%
53.299999241
0.8%
ValueCountFrequency (%)
75.907318121
0.8%
74.900001531
0.8%
74.099998471
0.8%
73.599998471
0.8%
73.51
0.8%
73.400001531
0.8%
73.300003052
1.6%
73.099998471
0.8%
731
0.8%
72.699996951
0.8%

Freedom
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7797820709
Minimum0.4779566526
Maximum0.9851777554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-04-11T19:20:08.262123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.4779566526
5-th percentile0.574779284
Q10.7158222795
median0.8104018569
Q30.859389782
95-th percentile0.9234937072
Maximum0.9851777554
Range0.5072211027
Interquartile range (IQR)0.1435675025

Descriptive statistics

Standard deviation0.1106291122
Coefficient of variation (CV)0.1418718336
Kurtosis-0.1772146347
Mean0.7797820709
Median Absolute Deviation (MAD)0.07544857264
Skewness-0.6069435133
Sum97.47275886
Variance0.01223880046
MonotonicityNot monotonic
2022-04-11T19:20:08.403809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7532133461
 
0.8%
0.81040185691
 
0.8%
0.84939658641
 
0.8%
0.96377468111
 
0.8%
0.89621716741
 
0.8%
0.8116706611
 
0.8%
0.85680198671
 
0.8%
0.78742790221
 
0.8%
0.92289680241
 
0.8%
0.83084261421
 
0.8%
Other values (115)115
92.0%
ValueCountFrequency (%)
0.47795665261
0.8%
0.48442915081
0.8%
0.52744680641
0.8%
0.53811371331
0.8%
0.55282521251
0.8%
0.5637986661
0.8%
0.57034790521
0.8%
0.59250479941
0.8%
0.59519076351
0.8%
0.59887552261
0.8%
ValueCountFrequency (%)
0.98517775541
0.8%
0.96377468111
0.8%
0.96201664211
0.8%
0.9387832881
0.8%
0.93561846021
0.8%
0.92570310831
0.8%
0.92364293341
0.8%
0.92289680241
0.8%
0.92216277121
0.8%
0.92086267471
0.8%

Generosity
Real number (ℝ)

HIGH CORRELATION

Distinct122
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.02769808311
Minimum-0.2534932792
Maximum0.3807405233
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)61.6%
Memory size1.1 KiB
2022-04-11T19:20:08.540630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.2534932792
5-th percentile-0.2250825614
Q1-0.1462403238
median-0.03603339568
Q30.07860356569
95-th percentile0.2041968763
Maximum0.3807405233
Range0.6342338026
Interquartile range (IQR)0.2248438895

Descriptive statistics

Standard deviation0.1368641557
Coefficient of variation (CV)-4.941286195
Kurtosis-0.2786622243
Mean-0.02769808311
Median Absolute Deviation (MAD)0.1140119918
Skewness0.5046217416
Sum-3.462260389
Variance0.01873179712
MonotonicityNot monotonic
2022-04-11T19:20:08.677992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.030673333444
 
3.2%
0.24762122331
 
0.8%
-0.23090977971
 
0.8%
0.24923355881
 
0.8%
-0.067120328551
 
0.8%
-0.25349327921
 
0.8%
-0.17876517771
 
0.8%
-0.032642815261
 
0.8%
0.38074052331
 
0.8%
-0.036033395681
 
0.8%
Other values (112)112
89.6%
ValueCountFrequency (%)
-0.25349327921
0.8%
-0.24707399311
0.8%
-0.24521587791
0.8%
-0.24293856321
0.8%
-0.23090977971
0.8%
-0.23050585391
0.8%
-0.22703394291
0.8%
-0.21727703511
0.8%
-0.20603477951
0.8%
-0.20424206551
0.8%
ValueCountFrequency (%)
0.38074052331
0.8%
0.30877295141
0.8%
0.28691646461
0.8%
0.24923355881
0.8%
0.24762122331
0.8%
0.24332368371
0.8%
0.20539546011
0.8%
0.19940254091
0.8%
0.18883281951
0.8%
0.18662165111
0.8%

Corruption
Real number (ℝ≥0)

HIGH CORRELATION

Distinct115
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7727376025
Minimum0.411346525
Maximum0.9367640018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-04-11T19:20:08.819270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.411346525
5-th percentile0.5487599611
Q10.7274513245
median0.7834294438
Q30.8512064219
95-th percentile0.9112290382
Maximum0.9367640018
Range0.5254174769
Interquartile range (IQR)0.1237550974

Descriptive statistics

Standard deviation0.1127060246
Coefficient of variation (CV)0.1458529057
Kurtosis1.954099411
Mean0.7727376025
Median Absolute Deviation (MAD)0.06496888399
Skewness-1.321158017
Sum96.59220031
Variance0.01270264799
MonotonicityNot monotonic
2022-04-11T19:20:08.965706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.782116115111
 
8.8%
0.65253901481
 
0.8%
0.73954063651
 
0.8%
0.79126876591
 
0.8%
0.86652499441
 
0.8%
0.65360605721
 
0.8%
0.7500262261
 
0.8%
0.86830550431
 
0.8%
0.83064633611
 
0.8%
0.71392822271
 
0.8%
Other values (105)105
84.0%
ValueCountFrequency (%)
0.4113465251
0.8%
0.4140211941
0.8%
0.41581019761
0.8%
0.41861134771
0.8%
0.46464160081
0.8%
0.51830381161
0.8%
0.54304605721
0.8%
0.57161557671
0.8%
0.58963197471
0.8%
0.59161680941
0.8%
ValueCountFrequency (%)
0.93676400181
0.8%
0.92633378511
0.8%
0.92565804721
0.8%
0.92519181971
0.8%
0.92334306241
0.8%
0.92042267321
0.8%
0.91133636241
0.8%
0.91079974171
0.8%
0.91072726251
0.8%
0.89538413291
0.8%

PositiveAffect
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7008945575
Minimum0.4209618866
Maximum0.9027721286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-04-11T19:20:09.104686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.4209618866
5-th percentile0.519479537
Q10.6151096821
median0.714645505
Q30.7863675952
95-th percentile0.8463468194
Maximum0.9027721286
Range0.4818102419
Interquartile range (IQR)0.1712579131

Descriptive statistics

Standard deviation0.1060357747
Coefficient of variation (CV)0.1512863434
Kurtosis-0.5670075549
Mean0.7008945575
Median Absolute Deviation (MAD)0.08397114277
Skewness-0.3108874643
Sum87.61181968
Variance0.01124358552
MonotonicityNot monotonic
2022-04-11T19:20:09.251745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.66924089191
 
0.8%
0.7457320691
 
0.8%
0.60036820171
 
0.8%
0.59711217881
 
0.8%
0.80642837291
 
0.8%
0.73656570911
 
0.8%
0.71023023131
 
0.8%
0.76898145681
 
0.8%
0.84646707771
 
0.8%
0.62505561111
 
0.8%
Other values (115)115
92.0%
ValueCountFrequency (%)
0.42096188661
0.8%
0.44980090861
0.8%
0.45518189671
0.8%
0.50245988371
0.8%
0.50972121951
0.8%
0.51544392111
0.8%
0.51912814381
0.8%
0.52088510991
0.8%
0.53932255511
0.8%
0.54090577361
0.8%
ValueCountFrequency (%)
0.90277212861
0.8%
0.89525455241
0.8%
0.87439584731
0.8%
0.87279176711
0.8%
0.84976416831
0.8%
0.84885060791
0.8%
0.84646707771
0.8%
0.84586578611
0.8%
0.84264230731
0.8%
0.84220093491
0.8%

NegativeAffect
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct125
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2882895778
Minimum0.1141231582
Maximum0.4950400293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-04-11T19:20:09.397614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.1141231582
5-th percentile0.1764634877
Q10.2315976173
median0.2790243477
Q30.3406215012
95-th percentile0.4251515806
Maximum0.4950400293
Range0.3809168711
Interquartile range (IQR)0.1090238839

Descriptive statistics

Standard deviation0.07722661158
Coefficient of variation (CV)0.2678786107
Kurtosis-0.4886000768
Mean0.2882895778
Median Absolute Deviation (MAD)0.04818814993
Skewness0.3230564878
Sum36.03619722
Variance0.005963949535
MonotonicityNot monotonic
2022-04-11T19:20:09.540347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2950635851
 
0.8%
0.23099088671
 
0.8%
0.34962767361
 
0.8%
0.27544006711
 
0.8%
0.40426206591
 
0.8%
0.2348256261
 
0.8%
0.2140047551
 
0.8%
0.34422588351
 
0.8%
0.32640707491
 
0.8%
0.21359951791
 
0.8%
Other values (115)115
92.0%
ValueCountFrequency (%)
0.11412315821
0.8%
0.14816001061
0.8%
0.16016410291
0.8%
0.16043831411
0.8%
0.16872118411
0.8%
0.17148640751
0.8%
0.17551217971
0.8%
0.18026871981
0.8%
0.18092127141
0.8%
0.18587909641
0.8%
ValueCountFrequency (%)
0.49504002931
0.8%
0.44612428551
0.8%
0.43853390221
0.8%
0.43714874981
0.8%
0.43394353991
0.8%
0.42652237421
0.8%
0.42582425481
0.8%
0.42246088391
0.8%
0.41607204081
0.8%
0.41449379921
0.8%

ConfidenceInGovernment
Real number (ℝ≥0)

HIGH CORRELATION

Distinct114
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4795587087
Minimum0.1109365299
Maximum0.9646904469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-04-11T19:20:09.688997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.1109365299
5-th percentile0.2235453367
Q10.3416800797
median0.4816823304
Q30.5904251933
95-th percentile0.8294826984
Maximum0.9646904469
Range0.8537539169
Interquartile range (IQR)0.2487451136

Descriptive statistics

Standard deviation0.1868817265
Coefficient of variation (CV)0.3896951992
Kurtosis-0.2707558388
Mean0.4795587087
Median Absolute Deviation (MAD)0.1301324368
Skewness0.4248766247
Sum59.94483859
Variance0.03492477971
MonotonicityNot monotonic
2022-04-11T19:20:09.826347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.481682330412
 
9.6%
0.77082109451
 
0.8%
0.73751693961
 
0.8%
0.45773753521
 
0.8%
0.67028856281
 
0.8%
0.51758992671
 
0.8%
0.92979305981
 
0.8%
0.6399807931
 
0.8%
0.29210972791
 
0.8%
0.83993500471
 
0.8%
Other values (104)104
83.2%
ValueCountFrequency (%)
0.11093652991
0.8%
0.12637971341
0.8%
0.13272963461
0.8%
0.16549026971
0.8%
0.2110006661
0.8%
0.21771809461
0.8%
0.22188259661
0.8%
0.23019629721
0.8%
0.23977972571
0.8%
0.24112360181
0.8%
ValueCountFrequency (%)
0.96469044691
0.8%
0.92979305981
0.8%
0.91333901881
0.8%
0.87664645911
0.8%
0.83993500471
0.8%
0.83927839991
0.8%
0.83773005011
0.8%
0.79649329191
0.8%
0.77082109451
0.8%
0.76835429671
0.8%

Region
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Sub-Saharan Africa
31 
Central and Eastern Europe
29 
Latin America and Caribbean
21 
Middle East and Northern Africa
15 
Western Europe
11 
Other values (5)
18 

Length

Max length31
Median length26
Mean length22.064
Min length12

Characters and Unicode

Total characters2758
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.6%

Sample

1st rowCentral and Eastern Europe
2nd rowLatin America and Caribbean
3rd rowCentral and Eastern Europe
4th rowAustralia and New Zealand
5th rowWestern Europe

Common Values

ValueCountFrequency (%)
Sub-Saharan Africa31
24.8%
Central and Eastern Europe29
23.2%
Latin America and Caribbean21
16.8%
Middle East and Northern Africa15
12.0%
Western Europe11
 
8.8%
Eastern Asia6
 
4.8%
Southern Asia5
 
4.0%
Southeastern Asia5
 
4.0%
North America1
 
0.8%
Australia and New Zealand1
 
0.8%

Length

2022-04-11T19:20:09.966323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-11T19:20:10.060438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
and66
16.6%
africa46
11.6%
europe40
10.1%
eastern35
8.8%
sub-saharan31
7.8%
central29
7.3%
america22
 
5.5%
latin21
 
5.3%
caribbean21
 
5.3%
asia16
 
4.0%
Other values (10)70
17.6%

Most occurring characters

ValueCountFrequency (%)
a394
14.3%
r277
 
10.0%
272
 
9.9%
n240
 
8.7%
e216
 
7.8%
t143
 
5.2%
i142
 
5.1%
d97
 
3.5%
E90
 
3.3%
A85
 
3.1%
Other values (19)802
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2093
75.9%
Uppercase Letter362
 
13.1%
Space Separator272
 
9.9%
Dash Punctuation31
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a394
18.8%
r277
13.2%
n240
11.5%
e216
10.3%
t143
 
6.8%
i142
 
6.8%
d97
 
4.6%
s83
 
4.0%
u82
 
3.9%
b73
 
3.5%
Other values (8)346
16.5%
Uppercase Letter
ValueCountFrequency (%)
E90
24.9%
A85
23.5%
S72
19.9%
C50
13.8%
L21
 
5.8%
N17
 
4.7%
M15
 
4.1%
W11
 
3.0%
Z1
 
0.3%
Space Separator
ValueCountFrequency (%)
272
100.0%
Dash Punctuation
ValueCountFrequency (%)
-31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2455
89.0%
Common303
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a394
16.0%
r277
11.3%
n240
 
9.8%
e216
 
8.8%
t143
 
5.8%
i142
 
5.8%
d97
 
4.0%
E90
 
3.7%
A85
 
3.5%
s83
 
3.4%
Other values (17)688
28.0%
Common
ValueCountFrequency (%)
272
89.8%
-31
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a394
14.3%
r277
 
10.0%
272
 
9.9%
n240
 
8.7%
e216
 
7.8%
t143
 
5.2%
i142
 
5.1%
d97
 
3.5%
E90
 
3.3%
A85
 
3.1%
Other values (19)802
29.1%

Interactions

2022-04-11T19:20:04.497168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:48.362946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:49.849052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:51.152654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:53.977117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:55.422050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:56.883046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:58.390465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:59.895298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:01.374661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:02.859557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:04.645834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:48.547798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:49.973982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:51.280641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:54.112715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:55.561866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:57.025085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:58.540925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:00.034372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:01.519902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:03.006934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:04.764983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:48.681644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:50.081006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:51.388035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:54.233072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:55.680867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:57.160289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:58.664019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:00.159808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:01.639721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:03.147349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:04.885877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:48.809859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:50.188616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:51.507785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:54.358103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:55.810728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:57.286251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:58.808239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:00.294375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:01.771405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:03.292765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:05.011019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:48.934822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:50.305291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:51.625996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:54.485231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:55.943229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:57.425272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:58.934617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:00.426585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:01.899600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:03.431139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:05.141026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:49.062994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:50.414547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:51.740375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:54.611112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:56.069962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:57.557557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:59.067481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:00.559595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:02.028752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:03.590959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:20:05.267455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-11T19:19:49.188252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-11T19:20:10.409952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-11T19:20:10.597842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-11T19:20:10.789425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexCountryHappinessGDPSocialSupportHealthFreedomGenerosityCorruptionPositiveAffectNegativeAffectConfidenceInGovernmentRegion
01Albania4.6395489.3761450.63769868.4000020.749611-0.0326430.8761350.6692410.3338840.457738Central and Eastern Europe
13Argentina6.0393309.8487090.90669968.5999980.831966-0.1826000.8410520.8094230.2917170.305430Latin America and Caribbean
24Armenia4.2877369.0810950.69792566.5999980.613697-0.1339580.8646830.6250140.4371490.246901Central and Eastern Europe
35Australia7.25703810.7065810.94995873.3000030.9105500.3087730.4113470.7800790.2253610.453407Australia and New Zealand
46Austria7.29372810.7240750.90621872.6999970.8900310.1311140.5183040.7475690.1802690.435908Western Europe
57Azerbaijan5.1522799.6707620.78703965.1999970.731030-0.2452160.6525390.5923590.1983190.766583Central and Eastern Europe
68Bahrain6.22732110.6756940.87574768.5000000.9058590.1281930.7821160.8135710.2897600.481682Middle East and Northern Africa
79Bangladesh4.3097718.1673470.71255363.7999990.8962170.0162690.6350140.5688270.2135060.876646Southern Asia
810Belarus5.5529159.7508000.90025665.8000030.620979-0.1291730.6541130.5409060.2327680.447916Central and Eastern Europe
911Belgium6.92834810.6609840.92163971.8000030.8568020.0517600.5430460.7863680.2335980.449732Western Europe

Last rows

df_indexCountryHappinessGDPSocialSupportHealthFreedomGenerosityCorruptionPositiveAffectNegativeAffectConfidenceInGovernmentRegion
115137United Arab Emirates7.03942011.1168180.83552766.9000020.9620170.1994030.7821160.7950350.2075980.481682Middle East and Northern Africa
116138United Kingdom7.10327310.5904460.93749572.0999980.8127330.2869160.4186110.7585720.2095720.440121Western Europe
117139United States6.99175910.9009060.92100368.4000020.8684970.1888330.6811910.8265550.2682690.386535North America
118140Uruguay6.3360109.9306850.91380268.9000020.897852-0.1014150.6265820.8358610.2803230.413032Latin America and Caribbean
119141Uzbekistan6.4214488.7408330.94213164.8000030.9851780.1165110.4646420.8389890.2027370.964690Central and Eastern Europe
120142Venezuela5.0707519.4392960.89587966.3000030.635505-0.2060350.8439690.7256430.3629850.241124Latin America and Caribbean
121143Vietnam5.1752798.7277590.82864667.6999970.812530-0.0306730.7821160.7207160.2790240.481682Southeastern Asia
122144Yemen3.2535609.4921260.78955555.9000020.595191-0.0306730.7821160.4551820.2950640.247787Middle East and Northern Africa
123145Zambia3.9327778.2131790.74375454.7999990.8231690.1289040.7395410.6846230.3871890.717004Sub-Saharan Africa
124146Zimbabwe3.6383007.5494910.75414755.0000000.752826-0.0696700.7512080.8064280.2240510.682647Sub-Saharan Africa